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Authors: Eduardo Alonso 1 and Esther Mondragón 2

Affiliations: 1 City University London, United Kingdom ; 2 Centre for Computational and Animal Learning Research, United Kingdom

ISBN: 978-989-8565-38-9

Keyword(s): Reinforcement Learning, Associative Learning, Agents and Multi-agent Systems.

Related Ontology Subjects/Areas/Topics: Agent Models and Architectures ; Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Autonomous Systems ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Multi-Agent Systems ; Software Engineering ; Symbolic Systems

Abstract: In this position paper we propose to enhance learning algorithms, reinforcement learning in particular, for agents and for multi-agent systems, with the introduction of concepts and mechanisms borrowed from associative learning theory. It is argued that existing algorithms are limited in that they adopt a very restricted view of what “learning” is, partly due to the constraints imposed by the Markov assumption upon which they are built. Interestingly, psychological theories of associative learning account for a wide range of social behaviours, making it an ideal framework to model learning in single agent scenarios as well as in multi-agent domains.

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Paper citation in several formats:
Alonso, E. and Mondragón, E. (2013). Associative Reinforcement Learning - A Proposal to Build Truly Adaptive Agents and Multi-agent Systems.In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8565-38-9, pages 141-146. DOI: 10.5220/0004175601410146

@conference{icaart13,
author={Eduardo Alonso. and Esther Mondragón.},
title={Associative Reinforcement Learning - A Proposal to Build Truly Adaptive Agents and Multi-agent Systems},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2013},
pages={141-146},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004175601410146},
isbn={978-989-8565-38-9},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Associative Reinforcement Learning - A Proposal to Build Truly Adaptive Agents and Multi-agent Systems
SN - 978-989-8565-38-9
AU - Alonso, E.
AU - Mondragón, E.
PY - 2013
SP - 141
EP - 146
DO - 10.5220/0004175601410146

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